J. David Holtzclaw,* Arri Eisen, Erika M. Whitney,* Meera Penumetcha

J. David Holtzclaw,* Arri Eisen, Erika M. Whitney,* Meera Penumetcha
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   Article Incorporating a New Bioinformatics Component into Genet-ics at a Historically Black College: Outcomes and Lessons  J. David Holtzclaw,* Arri Eisen, † Erika M. Whitney,* Meera Penumetcha,* J. Joseph Hoey, ‡ and K. Sean Kimbro § *School of Medicine and † Department of Biology, Emory University, Atlanta, GA 30322; ‡ Georgia Institute of Technology, Atlanta, GA 30332; and § Clark Atlanta University, Atlanta, GA 30314 Submitted April 12, 2005; Accepted September 21, 2005Monitoring Editor: Elizabeth Vallen Many students at minority-serving institutions are underexposed to Internet resources such asthe human genome project, PubMed, NCBI databases, and other Web-based technologies be-cause of a lack of financial resources. To change this, we designed and implemented a new bioinformatics component to supplement the undergraduate Genetics course at Clark AtlantaUniversity. The outcomes of the Bioinformatics course were assessed. During the first week of thesemester, students were assigned the Felder-Soloman’s Index of Learning Styles Inventory. Theoverwhelming majority of students were visual (82.1%) and sequential (75.0%) learners. Further-more, pre- and postcourse surveys were administered during the first and the last week of thecourse to assess learning, confidence level, and mental activity. These indicated students in-creased the number of hours spent using computers and doing homework. Students reportedconfidence in using computers to study genetics increased, enabling them to better visualize andunderstand genetics. Furthermore, students were more mentally engaged in a more sociallearning environment. Although the students appreciated the value of the bioinformatics com-ponent, they reported the additional work load was substantial enough to receive additionalcourse credit. INTRODUCTION Bioinformatics is the use of computer science, mathematics,and information technology to collect, to organize, and toanalyze large volumes of biological data. Biological datacome from a large array of subjects including cellular andmolecular biology, genetics, biochemistry, evolutionary bi-ology, physiology, and several others. Recent research ef-forts such as the Human Genome Project and new technol-ogy such as DNA microarrays have produced enormousvolumes of genetic information waiting to be mined byspecialized software. This has produced a growing demandfor trained bioinformaticians, making them one of the mostsought after and fastest growing sectors in biotechnology.According to a survey of 176 biotechnology companies (Vir-ginia Commonwealth University Center for Public Policy,2001), most firms plan on hiring two new employees in bioinformatics within the next 12 mo and seven more in thenext 5 yr at an average starting annual salary of $45,000 foran entry-level position (M.S. degree or less). Unfortunately,many academic institutions are not prepared to meet thisimmediate need. Hence, it is estimated that 20,000 jobs in bioinformatics will be left unfilled by 2005 (Eisenberg, 2002).As previously discussed in Cell Biology Education, histori-cally black colleges and universities (HBCUs) are doing theirpart to help America meet this need despite limited federalsupport (Suitts, 2003). Unfortunately, many HBCUs lack theresources to implement courses in bioinformatics. Further-more, faculty at many HBCUs developed their researchfocus before the evolution of bioinformatics. Hence, most biology students at HBCUs are not exposed to online re-sources such as the Human Genome Project, PubMed, Na-tional Center for Biotechnology Information (NCBI) data- bases, or other related tools (i.e., BLAST, Cn3D, etc.). Tochange this, postdoctoral fellows from the Fellowships inResearch and Science Teaching (FIRST) Program designed DOI: 10.1187/cbe.05–04–0071Address correspondence to: J. David Holtzclaw (david.holtzclaw1@—Life Sciences EducationVol. 5, 52–64, Spring 200652 © 2006 by The American Society for Cell Biology  and implemented a new bioinformatics component to sup-plement the undergraduate Genetics course at Clark AtlantaUniversity (CAU).FIRST is part of a National Institutes of Health initiativefrom the Minority Opportunities in Research Division of theNational Institute of General Medical Sciences. Grants,known as Institutional Research and Academic Career De-velopment Awards (IRACDA), from this program combinea traditional mentored postdoctoral research experience at aresearch institution (Emory University) with an experienceto develop teaching skills through innovative programs thatinvolve mentored teaching assignments at minority-servinginstitutions (MSIs; Holtzclaw et al., 2005). The objectives of this initiative are threefold: 1) to enhance research-orientedteaching at MSIs; 2) to increase the research and other skillsneeded by training scientists to conduct high-quality re-search in an academic environment; and 3) to promote link-ages between research-intensive institutions and MSIs thatcan lead to further collaborations in research and teaching.Finally, a desired long-term outcome is to increase the num- ber of well-qualified underrepresented minority studentsentering competitive careers in biomedical research (Na-tional Institute of General Medical Sciences, 2002).The specific goals of the bioinformatics component werethreefold: 1) to provide CAU students exposure and intro-ductory training in bioinformatics, demonstrating an alter-native career path; 2) to provide CAU students a moreinteractive, visually oriented, and discovery-based learningapproach to genetics; and 3) to allow CAU to assess the needfor incorporating bioinformatics at various levels into itscurriculum. The Biology Department at CAU was chosen forthis initiative because of its computer and distance-learningresources and the existence of a graduate program, whichtogether have the potential to establish CAU as a key seg-ment of the pipeline providing the private and public sectorswith well-trained minority bioinformaticians.Several strategies for incorporating bioinformatics into theundergraduate curriculum have previously been describedin Cell Biology Education (Campbell, 2003; Honts, 2003). Herewe address this issue from the perspective of an HBCU.Second, we examine the potential role of bioinformatics incomplementing and enhancing an undergraduate geneticscourse. Furthermore, we assess student comfort and confi-dence with the computer, Internet resources, and geneticsthrough pre- and postcourse surveys. We also assess thestudent’s learning styles, level of engagement in Bloom’staxonomy (Bloom and Krathwohl, 1984), effectiveness of thecourse and course Web site, and how well the course ful-filled the students’ career objectives.  METHODS AND ASSESSMENT Pilot Course A pilot bioinformatics component for the undergraduate Geneticscourse (Biology 312) was offered at CAU in Spring 2003. Participa-tion in the bioinformatics component, which occurred during therecitation period, was voluntary with extra credit given for submit-ted homework projects. The recitation classes were held in thedistance-learning laboratory at CAU. This facility holds 28 desktopcomputers with high-speed Internet access and a classroom projec-tor from which the instructor could project his/her laptop into thescreen to guide students through different exercises. In terms of information technology, this was an ideal setup.The pilot bioinformatics component contained four modules.These modules included a general introduction to PubMed (manyof our students had never used PubMed before this class), andtutorials on BLAST, GenBank, and Map Viewer. Fourteen studentsparticipated, and postcourse surveys were completed (our unpub-lished data). On the basis of student feedback, we made severalchanges to the content and logistics for the Spring 2004 course. It ishighly recommended that instructors run a pilot course on a smallnumber of students (5–10) to determine how much time per class toallow for logistical concerns (setting up computers, connecting tothe Intranet, accessing databases, etc.). Course Infrastructure Because of scheduling complexities of computer facilities, we had tofind a new location for the bioinformatics recitation class during theSpring 2004 semester. Available computer facilities were alreadyoverburdened and in continual use. Ideally, we wanted to developan educational environment in which bioinformatics could betaught in any of the four core laboratories and two lecture hallsprimarily used by the Biology Department at CAU. Conveniently,all these labs and classrooms exist on the same two, concurrentfloors of the science building. This facilitated the installation of newinformation technology resources. With the financial assistance of the FIRST Program, we installed a Cisco 1200 Series wireless localarea network (WLAN), 802.11g IOS (Cisco Systems, San Jose, CA)and purchased 22 Dell Inspiron laptops with internal, wireless,mini-PCI cards (1300 WLAN, 802.11g, Dell Computers, RoundRock, TX). These wireless, laptop computers were used in the bioin-formatics recitation held in the general biology laboratory space. Course Description and Content  Genetics was a three-credit course typically taught for 1.5 h twiceper week with a voluntary recitation class for 1 h once per week(Genetics course outline and schedule are given in Appendix TableA1). Historically, most of our genetics students are juniors with afew sophomores and seniors. The only course prerequisite was CellBiology. The current Biology Department curriculum has no com-puter or calculus course requirements, although they are stronglyrecommended. Therefore, we could not alter the course require-ments by requiring calculus or computer programming, althoughwe would for future courses (see Discussion section).The bioinformatics component was taught once per week duringthe recitation period of the Genetics course. The complete coursesyllabus as well as handouts, homework assignments, and supple-mental materials are all available on the course Web site (Holtzclaw,2004). The bioinformatics component consisted of 22% (200/900points) of the final Genetics grade. We covered introductory bioin-formatics such as how to access and interpret information from thepublicly available databases (PubMed, nucleotide, protein, andstructure databases, etc.). We chose topics that would be of interestto our student population (e.g., sickle cell anemia, diabetes, breastcancer, etc.). Each topic was presented through a case study, termed“module,” and case-based teaching pedagogy was used (Herreid,1994).Briefly, case-based teaching pedagogy includes the use of a con-crete, real-world problem (i.e., diseases, environmental conditions,etc.) to teach scientific theory or knowledge. In the context of aBioinformatics course, each case was designed to focus on a partic-ular macromolecule, related to a diseased state, and investigatedthrough a database. Students were presented a problem or case (i.e.,disease or environmental condition) and were required to use theNCBI databases to address it. During the course of solving the case,students would learn about a particular macromolecule and applycourse theories and concepts. For example, a sickle cell anemia casecan be used to teach students how to use the NCBI nucleotidedatabase by having them look up hemoglobin A (accession numberUsing Bioinformatics to Teach GeneticsVol. 5, Spring 2006 53  NM_000518) and HbS (accession number M25113) and compare thesequences until they find the point mutation for either amino acid ornucleotide sequence (see Appendix for more details on this case).We then demonstrated, using software and images available inprotein-structure databases, how that single point mutation resultsin a change in the three-dimensional conformation of the molecule.This case was a powerful example of how a single genetic mutationat the nucleotide level can cause conformational changes, resultingin a life-threatening disease. Although students can be exposed tothe same material through a traditional lecture format or by readinga textbook, our students learned this information in a rich, hands-oncontext and can apply these same skills to discover the mechanismsof other genetic diseases.Modules were organized to focus on a selected database and to build on previously discussed modules. For example, the secondmodule (sickle cell anemia) just described required understandingof the nucleotide database presented in the first module. Anothermodule focused on diabetes and engaged the OMIM (Online Men-delian Inheritance In Man) database and a specific journal article(PubMed) with questions for the students to answer. A third mod-ule on breast cancer introduced later in the course required the useof a previously used database (PubMed), as well as introduced newones (for protein sequences and protein structures).Importantly, we organized modules to align with content pre-sented in the Genetics course lecture (see Appendix for courseschedule). The sickle cell module also required knowledge of geneexpression (i.e., transcription and translation), which was covered inlecture during the weeks before the students did that module.Similarly, module 3 on mitochondrial DNA and module 4 on drugresistance corresponded to the related lecture topics of genomeanalysis, Mendelian genetics, the chromosomal basis of inheritance,and non-Mendelian inheritance. Likewise, the modules on the SNP(single nucleotide polymorphism) database (April 13) and genetherapy (April 20) were presented in sync with the lecture onpopulation genetics (April 20). The Genetics course textbook was Genetics , by Peter Russell, 5th edition (Russell, 1997).  Module Design and Format  A typical bioinformatics module consisted of students coming torecitation, checking out a laptop, and downloading the in-classassignment from the course Web site ( ϳ  jholtzc/Courses/Bio312/index.htm). Students ingroups of two or three then began working on module exercises (aportion of module 8 is given below as an example).  Module Exercise: Sample from Module 8 1. Create a folder on your desktop labeled structure. Save anystructures that you download to this folder.2. Click on the Structure database from the Entrez homepage, andfind the Cn3D tutorial. Download Cn3D if it’s not on yourcomputer already. Read the Cn3D “Introduction”:3. Cn3D can show you structures of which of the following (an-swer all that are correct)?A. linear DNAB. circular mRNAC. a specific chromosomeD. proteinsE. all of the above4. Read the first section, “Retrieving individual structures,” and doall of the exercises. What are the results of the PubMed, Protein, and Structuresearch for Hemoglobin A, HbA, Hb ␣ , and Hb-A? What is thenumber of results for each search? Give an example of each andthe species.6. Using the following methods, find the corresponding MMDBstructure files:A. Do an Entrez/PubMed database search to find the crystallo-graphic or NMR structures for PTEN, as in the example,Hemoglobin S (Hb S), and Hemoglobin A (Hb-A).i. Which database/query/links did you use for each pro-tein? Did you use any limits? If so, which ones?ii. How many results did you get for each protein?iii. Give a reference for a structure of each protein. Remem- ber, the left side is highlighted in green or yellow toindicate references that are available online.iv. Find 3 pictures/figures from the available references,save them in your structure file. You might have to openthe figure in a new window first, before you save the file.Make sure you name the file appropriately.7. A 3D structure is ideal, but not always available. What if thereis no structure file for the protein that you are looking at? Tofind mutations that have no crystal structure, you can use thereference protein’s known structure. Go to the Entrez site. Do anEntrez sequence neighbor search by doing a Protein database/Genpept search for human PTEN (use NP_000305 instead of O00633), Hemoglobin A (HbA) B chain, and HbS beta chain.i. Find and save the sequence for each protein in a text file.ii. How many 3D domains or chains does each protein(PTEN, Hemoglobin S, and Hemoglobin A) have?iii. Do a Blink to find similar structures. List the accessionnumber, gi number, and the protein description of 5PTEN mutants and 5 alpha and beta chain mutants for both Hemoglobins.iv. Describe the mutations for PTEN, Hemoglobin A, andHemoglobin S.v. Look at the 3D alignment of the 5 mutants/protein/chain that have crystal structures in Cn3D. Save to yourstructure file.The instructor(s) facilitated this process and then, after 20–30 min,assessed class progress, answered questions, and walked the stu-dents through the in-class assignment, providing additional infor-mation and examples. Then, the instructor reviewed the homeworkassignment, which typically was similar to or a continuation of thein-class assignment. The instructor also attended the 2-h, weeklyhelp session held during the evening, which was convenient forsome, but not all, of the students.  ILS and Pre- and Postcourse Surveys The Felder-Soloman’s Index of Learning Styles (ILS) is a self-scored,Web-based instrument that assesses learning style preferences onfour dimensions: sensing/intuiting, visual/verbal, active/reflec-tive, and sequential/global (Felder and Silverman, 1988). The ILShas been shown to be a suitable psychometric tool for evaluatinglearning styles of students (Zywno, 2003). During the first week of the semester, the 45 undergraduate students taking genetics (Biol-ogy 312) were assigned the ILS. In addition, pre- and postcoursesurveys were given during the first and last week of the course.Surveys were anonymous and postcourse surveys were analyzedafter grades were submitted. Differences in responses between theprecourse (aggregate) and the postcourse (also aggregate) responseswere determined by the chi-square test (StatSoft, 2004) using SPSSsoftware (Chicago, IL). Homework credit was given for completionof the ILS as well as the postcourse survey. RESULTS Although it is clear that bioinformatics is essential to acontemporary biology curriculum, a major question is howto include it most effectively. We measured the effectivenessof our particular approach by investigating how well it fit  J. D. Holtzclaw et al. CBE—Life Sciences Education54  with our students’ learning styles and examining a widerange of student learning variables before and after thecourse. Learning Styles Assessment  Twenty-eight students completed and submitted the ILS.The ILS scales are bipolar with mutually exclusive answersto each question (either A or B) with an odd number of questions (Zywno, 2003) for each dimension. Students ex-hibited three predominant learning styles (Figure 1). Theoverwhelming majority of students were visual (82.1%) andsequential (75.0%) learners, who showed a preference forsensory learning (67%). In other words, these students preferto visualize the course materials as diagrams, sketches, orschematics. Furthermore, they wanted the syllabus and classmaterial to follow a linear, stepwise, logical path and had atendency to learn material that had real-life relevance.On the basis of the ILS assessment shown in Figure 1, wesuggest our case-based, module approach to bioinformaticsenhanced students’ learning of genetics by providing infor-mation in the students’ preferred learning style in threeways. First, the bioinformatics component, through the useof graphical software such as Cn3D and a computer inter-face, was highly visual, allowing for our more visually ori-ented (Figure 1) students to study macromolecules from360°—a strategy more difficult to integrate within a tradi-tional lecture format. Second, our case-based method pro-vides students with real-world application of geneticsthrough examples such as diabetes and gene therapy. Third,our systematic, stepwise approach to the implementation of different databases provides a logical progression of infor-mation that would be beneficial to our sequential learners.Results of the pre- and postcourse surveys strongly supportthese conclusions. By presenting information in alignmentwith the students’ preferred learning styles, we transfer theeffort and energy students exert from formatting informa-tion to comprehension and application. Instructors should be careful not to fall into the trap of using only one or twopreferred learning styles of the class, but to incorporate asmany learning styles as possible to reach every student inthe class. Pre- and Postcourse Surveys Forty students completed the precourse survey, and 32 stu-dents completed the postcourse survey. The goal of thesesurveys was to provide self-reported measures of studentlearning, level of mental activity, computer confidence,value of bioinformatics in relation to their educational andcareer goals, effectiveness of the course Web site, and ratingsof instructor attributes. In both pre- and postcourse surveys,students were asked to “estimate your confidence level rightnow,” on a five-point scale where 5 ϭ high and 1 ϭ low, inusing various tools to study genetics or molecular biology.Results are given in Figure 2. After the class, 60.6% of students rated their level of confidence as high or good inusing the computer to study genetics, a significant increasefrom the 25% who gave it this rating in the precourse survey(  p Յ 0.005, Figure 2A). Similarly, 56% of students rated theirconfidence level as high or good postcourse compared withonly 30% precourse in using Internet databases, tools, andsoftware to study genetics (  p Յ 0.04, Figure 2B).Because 82% of our students had a visual learning stylepreference (Figure 1), we assessed whether the visual natureof bioinformatics would aid them in learning genetics. Byvisual nature of bioinformatics, we mean the computer- based tools such as Cn3D and MapViewer, which presentinformation graphically. When asked about their confidencelevel in using Internet databases, tools, and software toeffectively visualize genetics, 53.1% rated their confidencelevel as high or good postcourse compared with 32.5% pre-course (Figure 2C, p Յ 0.05). In the surveys, we did notdefine what it means to “effectively visualize genetics,”leaving it up to the student to define. Clearly, from thepostcourse responses, “effectively visualize genetics” wasdefined by the student as the approach used in the bioinfor-matics recitation (i.e., using computer technology and data- bases to learn concepts in genetics). This definition may have been more ambiguous in the precourse survey, potentiallyleading to the sharp increase in confidence level.Finally, we assessed the use of case-based learning in ourcourse to increase their understanding of genetics (Figure2D). Postcourse, 86% of the students rated their confidencelevel in using problem-based learning to understand genet-ics as moderate, good, or high as compared with 53% pre-course (  p Յ 0.02). Overall, in Figure 2, one sees a “leftward”shift to high, good, and moderate from moderate, low, andfair in the students’ responses postcourse compared withprecourse. Therefore, we concluded that the Bioinformaticsprimer enabled the students to better visualize and under-stand molecular structures, thus enhancing their learning of genetics.We also assessed whether the bioinformatics componentincreased student computer usage. Based on precourse sur-vey results, 67.5% of the students had never used computersin any biology class before (our unpublished data). As adirect result of this course, students spent more time on thecomputer (Figure 3A, p Յ 0.001) and the Internet (Figure 3B).Although we do not know for certain if this extra time wasacademically related or not, several students did mention tous, both verbally and in postcourse evaluations (our unpub- Figure 1. Learning style preferences. The Felder-Soloman’s Indexof Learning Styles (ILS) is a self-scored, Web-based instrument thatassesses preferences on four dimensions: sensing/intuiting, visual/verbal, active/reflective, and sequential/global. The ILS scales are bipolar with mutually exclusive answers to each question (either Aor B) with an odd number of questions for each dimension. Twenty-eight students completed and submitted the ILS. The percentage of students that fell into each dimension is given.Using Bioinformatics to Teach GeneticsVol. 5, Spring 2006 55  lished data), that they did use the NCBI databases for other biology courses. Students also spent more time on home-work (Figure 3C), which is probably due to a combination of the increased workload required for this course as well asstudents taking more core biology courses as they advancedin the degree program. Although students were spendingmore time on the computer and doing homework, there waslittle to no change in the amount of time they spent infinancially compensated activities (jobs, work study, etc.) orextracurricular activities (student organizations, sports,church-related activities, etc.) between pre- and postcourseevaluations (our unpublished data).We also assessed the effectiveness of the course Web site by asking a series of questions shown in Table 1. Studentswere asked “To what extent did utilizing the course Website. . . . ” and were given a five-point scale that ranged from5 ϭ greatly to 1 ϭ not at all. The Web site promoted greateraccess to the course materials (84% responded greatly ormoderately), and connecting to the NCBI Web site (77%responded greatly or moderately). In addition, the Web siteallowed students to schedule time for the course relative totheir work and personal responsibilities (66% respondedgreatly or moderately) while providing background andadditional information outside of lecture (71% respondedgreatly or moderately). Although these results were as ex-pected, we were surprised to find that students credited thecourse Web site with greatly or moderately increasing inter-actions among students enrolled in the course (71%) andworking collaboratively with other students (79%). Further-more, students credited the Web site for greatly or moder-ately increasing their effectiveness to organize or expresstheir comments or questions (66%) or seek answers to theirquestions (69%). Similar to and in support of results ob-tained in Figure 2, the course Web site either greatly ormoderately enhanced the students’ ability to visualize theideas and concepts taught in the Genetics course (72%). Half the students (48%) responded that the Web site greatly ormoderately increased their understanding of genetics.Although the surveys were anonymous and postcoursesurveys were analyzed after grades were submitted, it ispossible that the students simply gave us the answers theythought we wanted or just simply filled in bubbles ran- Figure 2. Pre- and postcourse survey results on students’ confidence levels. In both pre- and postcourse surveys, students were asked to“estimate your confidence level you have right now in your knowledge and skills in. . . (one answer for each item)”: (A) computers to studygenetics or molecular biology; (B) Internet databases, tools, software, and technology to study genetics or molecular biology; (C) Internetdatabases, tools, software, and technology to effectively visualize genetics or molecular biology; (D) problem- or case-based learning to betterunderstand genetics. To respond, students were given a five-level scale that ranged from high to low (NA ϭ no response). Forty studentscompleted the precourse survey, and 32 students completed the postcourse survey. J. D. Holtzclaw et al. CBE—Life Sciences Education56
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